18 citations · 27 across the 4 of their papers we have counts for
5 papers
Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review
Teng Liu, Wenhao Tan, Xiaolin Tang +3
Motivated by the concerns on transported fuel consumption and global air pollution, industrial engineers, and academic researchers have made many efforts to construct more efficien…
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning
Teng Liu, Wenhao Tan, Xiaolin Tang +2
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology. This approach is bi…
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle
Xiaowei Guo, Teng Liu, Bangbei Tang +4
This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL). This work aims to…
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition
Teng Liu, Xiaolin Tang, Jiaxin Chen +3
Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission red…
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences
Hao Chen, Xiaolin Tang, Guo Hu +1
Development of hybrid electric vehicles depends on an advanced and efficient energy management strategy (EMS). With online and real-time requirements in mind, this article presents…